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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- marsyas/gtzan |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilhubert-finetuned-gtzan |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilhubert-finetuned-gtzan |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8818 |
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- Accuracy: 0.85 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 17 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.5851 | 1.0 | 113 | 1.7243 | 0.5 | |
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| 1.2937 | 2.0 | 226 | 1.2310 | 0.68 | |
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| 0.9718 | 3.0 | 339 | 0.8918 | 0.76 | |
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| 0.6613 | 4.0 | 452 | 0.6837 | 0.81 | |
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| 0.3693 | 5.0 | 565 | 0.6250 | 0.82 | |
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| 0.2991 | 6.0 | 678 | 0.5740 | 0.82 | |
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| 0.1381 | 7.0 | 791 | 0.5874 | 0.83 | |
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| 0.2047 | 8.0 | 904 | 0.5824 | 0.86 | |
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| 0.1192 | 9.0 | 1017 | 0.7106 | 0.83 | |
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| 0.0652 | 10.0 | 1130 | 0.6576 | 0.87 | |
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| 0.0105 | 11.0 | 1243 | 0.8236 | 0.84 | |
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| 0.0074 | 12.0 | 1356 | 0.7874 | 0.85 | |
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| 0.0064 | 13.0 | 1469 | 0.9066 | 0.84 | |
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| 0.0041 | 14.0 | 1582 | 0.8426 | 0.85 | |
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| 0.0038 | 15.0 | 1695 | 0.8676 | 0.84 | |
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| 0.0039 | 16.0 | 1808 | 0.8820 | 0.85 | |
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| 0.0036 | 17.0 | 1921 | 0.8818 | 0.85 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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